Randomized controlled trial of an internet-based self-guided hand exercise program to improve hand function in people with systemic sclerosis: the Scleroderma Patient-centered Intervention Network Hand Exercise Program (SPIN-HAND) trial.

Background: Systemic sclerosis (scleroderma; SSc) is a rare autoimmune connective tissue disease. Functional impairment of hands is common. The Scleroderma Patient-centered Intervention Network (SPIN)-HAND trial compared effects of offering access to an online self-guided hand exercise program to usual care on hand function (primary) and functional health outcomes (secondary) in people with SSc with at least mild hand function limitations.

Randomized feasibility trial of the Scleroderma Patient-centered Intervention Network hand exercise program (SPIN-HAND).

Purpose: The Scleroderma Patient-centered Intervention Network (SPIN) online hand exercise program (SPIN-HAND), is an online self-help program of hand exercises designed to improve hand function for people with scleroderma. The objective of this feasibility trial was to evaluate aspects of feasibility for conducting a full-scale randomized controlled trial of the SPIN-HAND program.

Materials And Methods: The feasibility trial was embedded in the SPIN cohort and utilized the cohort multiple randomized controlled trial (cmRCT) design.

Randomized feasibility trial of the Scleroderma Patient-centered Intervention Network Self-Management (SPIN-SELF) Program.

Background: The Scleroderma Patient-centered Intervention Network (SPIN) developed an online self-management program (SPIN-SELF) designed to improve disease-management self-efficacy in people with systemic sclerosis (SSc, or scleroderma). The aim of this study was to evaluate feasibility aspects for conducting a full-scale randomized controlled trial (RCT) of the SPIN-SELF Program.

Methods: This feasibility trial was embedded in the SPIN Cohort and utilized the cohort multiple RCT design.

The Scleroderma Patient-centered Intervention Network Self-Management (SPIN-SELF) Program: protocol for a two-arm parallel partially nested randomized controlled feasibility trial with progression to full-scale trial.

Background: Systemic sclerosis (scleroderma; SSc) is a rare autoimmune connective tissue disease. We completed an initial feasibility trial of an online self-administered version of the Scleroderma Patient-centered Intervention Network Self-Management (SPIN-SELF) Program using the cohort multiple randomized controlled trial (RCT) design. Due to low intervention offer uptake, we will conduct a new feasibility trial with progression to full-scale trial, using a two-arm parallel, partially nested RCT design.

The Scleroderma Patient-Centered Intervention Network Self-Management Program: Protocol for a Randomized Feasibility Trial.

Background: Systemic sclerosis (SSc), or scleroderma, is a rare disease that often results in significant disruptions to activities of daily living and can negatively affect physical and psychological well-being. Because there is no known cure, SSc treatment focuses on reducing symptoms and disability and improving health-related quality of life (HRQoL). Self-management programs are known to increase self-efficacy for disease management in many chronic diseases.

Validating Automated Sentiment Analysis of Online Cognitive Behavioral Therapy Patient Texts: An Exploratory Study.

Introduction: Sentiment analysis may be a useful technique to derive a user's emotional state from free text input, allowing for more empathic automated feedback in online cognitive behavioral therapy (iCBT) interventions for psychological disorders such as depression. As guided iCBT is considered more effective than unguided iCBT, such automated feedback may help close the gap between the two. The accuracy of automated sentiment analysis is domain dependent, and it is unclear how well the technology is applicable to iCBT.

Predictive modeling in e-mental health: A common language framework.

Recent developments in mobile technology, sensor devices, and artificial intelligence have created new opportunities for mental health care research. Enabled by large datasets collected in e-mental health research and practice, clinical researchers and members of the data mining community increasingly join forces to build predictive models for health monitoring, treatment selection, and treatment personalization. This paper aims to bridge the historical and conceptual gaps between the distant research domains involved in this new collaborative research by providing a conceptual model of common research goals.

Predicting therapy success for treatment as usual and blended treatment in the domain of depression.

In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression.

Assessment of temporal predictive models for health care using a formal method.

Recent developments in the field of sensor devices provide new possibilities to measure a variety of health related aspects in a precise and fine-grained manner. Subsequently, more empirical data will be generated than ever before. While this greatly improves the opportunities for creating accurate predictive models, other types of models besides the more traditional machine learning approaches can provide insights into temporal relationships in the data.